A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions

Huang, Cheng-Geng; Huang, Hong‐Zhong; Li, Yanfeng

doi:10.1109/tie.2019.2891463

Cited by 324 publications

(116 citation statements)

References 35 publications

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“…With the selected nine-dimensional informative sensors, HI that describes the degradation of the engine is constructed as Equation (12). Data matrices for healthy and failed states of the engine are respectively created using the nine-sensor measurements of the initial five cycles and the failure cycle of all the engines.…”

Section: Results and Analysis On Degradation Modelingmentioning

confidence: 99%

“…Among the data-driven prognostic methods, machine learning and statistical approaches are two popular branches. The common machine learning techniques used for prognostics include support vector regression [10], artificial neural network [11,12], fuzzy logic [13], etc. Machine learning methods are capable of dealing with prognostic issues of complex systems, but the predicted results are hard to be explained because of the lack of transparency.…”

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confidence: 99%

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Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis

Zhang

Zheng

Huang

et al. 2020

Sensors

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Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to run-to-failure data sets of C-MAPSS test-bed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.Sensors 2020, 20, 920 2 of 21 reducing unnecessary maintenance and minimizing operational costs, prognostics have been an active research field for aircraft engine applications [3].Generally, the existing methods pertaining to aircraft engine prognostics can be classified into two categories: model-based and data-driven methods. For model-based methods, a mathematical model that can describe the engine health degradation process that is normally required to be constructed according to physical failure characteristics before prediction. Some typical model-based methods have been developed for engine health estimation and RUL prediction, such as the Markov model-based [4,5] and particle filtering-based method [6,7]. While model-based methods enable improved accuracy of engine prognostics, certain assumptions and simplifications of the adopted models may pose limitations on their practical deployment. With the rapid development of data mining techniques and the growing availability of health monitoring data, data-driven methods attract increasing attention. Data-driven methods utilize the information extracted or learned from observed data to identify the degradation behavior and predict the future health condition without using any particular physical model [8,9]. In this view, data-driven methods may be the more applicable prognostic solution for complicated systems, as aero-engines that have limited knowledge of physics-of-failure but have massive multi-sensor monitoring data. Among the data-driven prognostic methods, machine learning and statistical approaches are two popular branches. The common machine learning techniques used for prognostics include support vector regression [10], artificial neural network [11,12], fuzzy logic [13], etc. Machine learning methods are capable of dealing with prognostic issues of complex systems, but the predicted results are hard to be explained because ...

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Section: Results and Analysis On Degradation Modelingmentioning

confidence: 99%

mentioning

confidence: 99%

Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis

Zhang

Zheng

Huang

et al. 2020

Sensors

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“…LSTM also has been used in FD-related research. In [ 61 ], Huang et al adopted a BLSTM-based method to solve the prognostic problem of aircraft engine remaining useful life (RUL). In [ 62 ], Yang et al proposed a method for FD for aircraft electromechanical actuators that used LSTM to analyze the correlation of sensors.…”

Section: Related Workmentioning

confidence: 99%

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Gao

Wei

Zhou

et al. 2020

Sensors

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In this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (MEMS) inertial sensors using a bidirectional long short-term memory (BLSTM)-based Hilbert–Huang transform (HHT) and a convolutional neural network (CNN). First, the method for fault diagnosis of inertial sensors is formulated into an HHT-based deep learning problem. Second, we present a new BLSTM-based empirical mode decomposition (EMD) method for converting one-dimensional inertial data into two-dimensional Hilbert spectra. Finally, a CNN is used to perform fault classification tasks that use time–frequency HHT spectrums as input. According to our experimental results, significantly improved performance can be achieved, on average, for the proposed BLSTM-based EMD algorithm in terms of EMD computational efficiency compared with state-of-the-art algorithms. In addition, the proposed fault diagnosis method achieves high accuracy in fault classification.

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“…Due to the influence of the production process, working environment, and stress excitation, the parameters of electronic components fluctuation sometimes exceed the rated range and causes system failure. 18,19 The tolerance design has led to drift failure of electronic circuits, which is not easy to be diagnosed by conventional methods. Moreover, too small tolerance design will lead to excessive system design cost.…”

Section: Reliability and Optimization Design Based On Tolerancementioning

confidence: 99%

Tolerance‐based reliability and optimization design of switched‐mode power supply

Zeng

et al. 2019

Quality & Reliability Eng

Self Cite

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Due to its high efficiency and low power consumption, switched‐mode power supply (SMPS) represents the development trend of the stabilized voltage power supply. However, tolerance has become one of the key factors in the design of SMPS because of the process fluctuation of electronic components, unstable input parameters of the circuit system, influence of working conditions and environment, and the effect of aging. In order to improve the reliability of SMPS and reduce the manufacturing cost, this paper proposes a reliability analysis and optimization design method based on the tolerance and sensitivity analysis. Finally, this method is applied to the tolerance design for the positive switching power supply of the SMPS circuits, and the optimal tolerance design scheme is obtained. Furthermore, the reliability and probability density curves are evaluated.

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A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions

Cited by 324 publications

References 35 publications

Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis

Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Tolerance‐based reliability and optimization design of switched‐mode power supply

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